Сравнение на методи

	Байесов модел на структурен векторна авторегресия (B-SVAR)	Байесов модел на векторна авторегресия (BVAR)
Област	Иконометрия	Иконометрия
Семейство	Regression model	Regression model
Година на възникване	1998–2005	1984
Създател	Sims & Zha (1998); Uhlig (2005) for sign-restriction identification	Doan, Litterman & Sims
Тип	Structural multivariate time-series model	Multivariate time-series model
Основополагащ източник	Sims, C. A., & Zha, T. (1998). Bayesian methods for dynamic multivariate models. International Economic Review, 39(4), 949–968.	Doan, T., Litterman, R., & Sims, C. (1984). Forecasting and conditional projection using realistic prior distributions. Econometric Reviews, 3(1), 1–100.
Други названия	Bayesian SVAR, B-SVAR, Bayesian structural VAR, Bayesian identified VAR	BVAR, Bayesian VAR, Bayesian vector autoregressive model, BVAR model
Свързани	6	5
Резюме	The Bayesian Structural Vector Autoregression model combines the structural identification of SVAR with Bayesian prior distributions over parameters. It estimates causal impulse responses between multiple time series while incorporating prior economic knowledge and producing full posterior uncertainty bands rather than point estimates alone.	The Bayesian Vector Autoregression (BVAR) model extends the classical VAR framework by incorporating prior beliefs about the model coefficients. Priors — most commonly the Minnesota prior — shrink VAR coefficients toward economically sensible values, dramatically reducing overfitting and improving out-of-sample forecast accuracy even when the number of variables is large.